8090 results about "Image frame" patented technology

Disclosed in the present application is a skeleton detection and fall detection method based on improved spatio-temporal adaptive graph convolution. The method comprises the steps of: S1, collecting image data to acquire data of each image frame; S2: using a pre-trained yolov5 target person detection model to detect whether a target person is present in the data of each image frame, and if a target person is present, turning to step S3, and if no target person is present, ending the process; S3: for each detected target person, using a Deepsort target tracking algorithm to perform target tracking to obtain a tracking result, calculating the similarity to obtain the result of target association, and updating trajectory information of each target person; and S4: performing pose recognition on each target person on the basis of the trajectory information, using a spatio-temporal adaptive graph convolutional network to extract a feature vector of a pose, and using a classifier to perform human body behavior classification and recognition, in order to determine whether the target person has experienced a fall incident. The method achieves higher accuracy and robustness.

The invention provides a crop whole growth cycle identification method and system based on deep learning, and the method comprises the steps: obtaining a multispectral image sequence of a target crop in a continuous time period through an image collection device, and carrying out the standardized illumination adjustment processing of each image frame in the multispectral image sequence, obtaining a standard illumination image set corresponding to the multispectral image sequence; carrying out crop region segmentation processing on each image, extracting a local feature region related to a target crop, generating a standardized crop image set containing the local feature region, inputting the standardized crop image set into a pre-trained multi-task deep learning model, extracting combined features through parallel convolution branches, and carrying out image segmentation processing on the combined features; and executing cross-stage correlation analysis in the full connection layer, outputting a multi-task classification result corresponding to the target crop growth cycle, and generating a stage identification report corresponding to the target crop full growth cycle. According to the invention, the crop whole growth cycle identification precision and the agricultural management efficiency can be improved.

The invention discloses a digital human real-time interaction system and method. The interaction system comprises terminal equipment and a cloud server. The terminal device receives voice input by a user and transmits the voice to the cloud server. The cloud server comprises a video acquisition and processing module, an intelligent question and answer and voice generation module, an audio processing module and an image rendering module. A video capture and processing module captures original video data indicative of an object of a digital figure and generates successive image frames. The intelligent question and answer and voice generation module converts the voice into a text; performing semantic analysis on the text, understanding the intention of the user, and generating a corresponding reply text; the reply text is also converted into a reply voice. The audio processing module processes the reply voice and the continuous image frames to generate first video data. And the image rendering module fuses the first video data with the virtual background, and performs real-time processing on the fused video to generate a response video. And the terminal equipment outputs the response video.

The embodiment of the invention discloses a bridge bolt monitoring image recognition method and system based on deep learning, and belongs to the technical field of bridge image processing. The method comprises the following steps: acquiring a multi-angle monitoring image set of a bridge bolt part through an image acquisition device, and preprocessing the monitoring image set to generate a standardized target image sequence; positioning a bolt monitoring area for each image frame in the target image sequence based on a pre-trained positioning model, and generating an area coordinate set containing bolt position information; inputting the region coordinate set into a state recognition model, extracting structural state features of each bolt monitoring region, and generating a looseness degree prediction result of the bolt part according to the structural state features; according to a matching result of the looseness degree prediction result and a preset safety threshold range, a bolt state abnormity alarm signal is output; and associating the bolt state abnormity alarm signal to the corresponding monitoring image frame, and generating a monitoring report containing a visual mark.

The invention relates to the technical field of intelligent control, in particular to a self-adaptive camera pose intelligent adjusting system which comprises a dynamic recognition module, a pose adjusting module, a mechanism control module, an information filtering module and a feedback association module. According to the method, the foreground pixel brightness gradient and the boundary continuity in the image frame are extracted, the target displacement path can be recognized, the tracking information is constructed, the target coherent perception under rapid scene change is guaranteed, the attitude parameters and the target motion direction and speed are combined, the offset correction instruction is generated, the response stability of the dynamic target is improved, and the target tracking accuracy is improved. The execution structure feedback is used for correcting control output, reducing the driving error accumulation risk, eliminating non-target disturbance through image boundary definition and motion consistency, improving data purity and combining a view angle execution state and path information to realize synchronous control of tracking and view angle adjustment; and the response efficiency and the following robustness of the system in a dynamic environment are enhanced.

A system to detect, classify and track small objects in an image frame. The system includes an image data system and image analytic system. The image data system receives real-time image data from an image capturing system and inputs the image data to image analytic system. The image analytic system performs the object detection, classification and tracking of objects in an image frame using a combination of techniques.

The invention relates to the technical field of monitoring analysis, in particular to a park safety monitoring method and system based on dynamic visual identification. The method comprises the following steps: acquiring a plurality of park monitoring videos based on park monitoring equipment; performing three-dimensional illumination migration rendering on the plurality of park monitoring videos, and performing low-frame smooth completion so as to generate a time sequence monitoring image frame sequence; carrying out spatial position positioning calculation on the park monitoring equipment, carrying out topological association network reconstruction, and constructing an omnibearing monitoring perception topological map; performing dynamic object visual detection on the time sequence monitoring image frame sequence, and performing frame-by-frame optical flow tracking analysis to generate a dynamic target optical flow trajectory; and carrying out multi-time-point sampling on the dynamic target optical flow trajectory, and then carrying out mutation point identification so as to generate an optical flow trajectory mutation point. According to the invention, efficient park safety monitoring analysis and high-precision safety early warning are realized.

The present disclosure relates to the field of computer vision and discloses a three-dimensional dynamic scene reconstruction method and apparatus, and a storage medium. The three-dimensional dynamic scene reconstruction method comprises: acquiring synchronized videos of a plurality of viewpoints of a dynamic scene; computing matching points between video images of different viewpoints, and estimating intrinsic and extrinsic parameters of each camera; obtaining a Gaussian splatting point set {p0} on the basis of a sparse point cloud constructed according to the depth of each matching point; for the first image frame of each video, using {p0} to perform static training thereon, to obtain a Gaussian splatting point set {p}; for the remaining image frames, dividing {p} into a static point set {S} and a dynamic point set {D}, performing dynamic training on {D}, and constructing a dynamic Gaussian splatting point set {P} from {p}, {S}, and the final {D}; and, in view of the intrinsic and extrinsic parameters of each camera, rendering {P} using a Gaussian splatting rendering pipeline, to obtain rendered images at different moments from new viewpoints.

The invention relates to the field of display screen pixel calibration, in particular to a pixel calibration method and system of a high-precision liquid crystal display screen. The method comprises the following steps: acquiring a real-time playing animation image of a high-precision liquid crystal display screen based on an internal sensor of the display screen; carrying out image scene distortion feature mining on the real-time playing animation image so as to obtain a comprehensive scene distortion feature; performing frame-by-frame pixel optical tracking on the real-time playing animation image, and performing frame-by-frame rolling offset vector calculation so as to construct a pixel point time sequence offset trajectory diagram; carrying out regional dynamic optical flow estimation on the pixel point time sequence offset trajectory diagram, carrying out dynamic trajectory distortion modeling based on comprehensive scene distortion characteristics, and constructing a pixel point offset trajectory distortion model; and carrying out dynamic color change perception on the pixel point offset track distortion model so as to generate a self-adaptive pixel point track offset compensation value. According to the invention, efficient and accurate pixel calibration is realized, and the dynamic visual effect is improved.

The invention discloses an efficient 3D Gaussian scene reconstruction method based on multi-modal depth distribution supervision, and compared with the prior art, pixel-level depth dense point clouds are directly extracted in a Gaussian initialization stage, a multi-modal depth distribution map is generated, random initialization of dense Gaussian positions according to COLMAP sparse point clouds is effectively avoided, and the reconstruction efficiency is improved. The problems that under the condition of few views, point clouds obtained by COLMAP are too sparse, provided geometric constraints are insufficient, and meanwhile randomly initialized Gaussian blocks the point clouds to move to the correct position due to the local minimum value are solved. According to the method, an image segmentation algorithm based on edge detection is applied to the image frame, a large number of Gaussian primitives which are far away from edge points and are similar in depth are fused, the 3D Gaussian number is effectively pruned, and memory consumption of a GPU is saved. According to the method, extra isotropic regularization loss is introduced into global gauss, so that the problem of Gaussian distribution in which artifacts are generated along the line-of-sight direction and the height is lengthened is avoided.

The invention belongs to the technical field of fire safety, and discloses a fire-fighting equipment detection early warning method and system based on a visual camera, and the method comprises the following steps: obtaining multi-focal-length image frames of a fixed spray head and a fire extinguisher edge region, judging the continuous change of gray scale in the continuous image frames, and carrying out the detection early warning of the fire-fighting equipment based on the visual camera. Comparing the gray level distribution difference under the long focus and the short focus, identifying the dynamic trajectory of the contour line angular point of the fire-fighting equipment association structure, analyzing the delay of the function action response, marking as an abnormal region, and generating a fire-fighting equipment collaborative early warning information set; according to the invention, by extracting and analyzing the multi-focal-length image frames of the edge areas of important facilities such as a fixed spray head and a fire extinguisher, fine changes of the fire-fighting facilities under differentiated focal lengths can be captured, working states and potential faults of the fire-fighting facilities can be judged, functional abnormalities caused by equipment aging or damage can be recognized in advance, early warning can be carried out in real time, and the safety of the fire-fighting facilities can be improved. And a higher guarantee level is brought to fire safety.

The invention discloses an underwater vision-inertia-acoustic odometer positioning method fused with a DVL, and belongs to the technical field of robot navigation positioning. According to the method, firstly, an underwater image acquired by a camera and carrier motion data acquired by an IMU and a DVL are used as input information to carry out time alignment and space coordinate system conversion; performing feature point extraction on the image to construct a visual re-projection residual error, and dynamically adjusting a weight ratio of visual information based on the number of feature points and the re-projection residual error; calculating IMU data increment between adjacent frames based on pre-integration to construct an inertial residual error; calculating the displacement change between image frames according to the speed information measured by the DVL to construct a DVL residual error; establishing a nonlinear least square optimization function based on Bayesian maximum posteriori estimation; vision, inertia and acoustic speed measurement data are fused at the rear end through a sliding window mechanism, a multi-constraint joint optimization model is constructed, and the robustness and accuracy of a vision-inertia-acoustic odometer system in the underwater environment are improved.

The invention relates to the technical field of tunnel engineering construction, and discloses an intelligent material transportation and safety monitoring system and method for shield construction, and the system comprises a visual perception unit, a sensor network module, an AI analysis center module, a safety decision module and a human-computer interaction interface. According to the invention, data acquisition is carried out through the visual perception unit and the sensor network module, multi-target detection operation is carried out on image frames through the AI analysis center module after target identification and track prediction, target types, space coordinates, contour boundaries and confidence coefficients are identified and extracted, and safety judgment and early warning output are carried out. According to the invention, by integrating the multi-view camera equipment and the UWB, GNSS and other sensors and adopting a deep learning target detection algorithm, high-precision identification and continuous tracking can be carried out on construction site personnel, equipment, segments and other key objects, and accurate input is provided for subsequent risk analysis.

The invention discloses a three-dimensional scene reconstruction method and device based on structure perception. The method comprises the following steps: constructing a sparse point cloud generation model according to a diffusion model based on a Unet architecture; inputting the information into a sparse point cloud generation model, and generating camera internal and external parameters and sparse point cloud data of the RGB image frame under the corresponding view angle; constructing a rough three-dimensional structure grid according to the sparse point cloud data; carrying out random sampling on the rough three-dimensional structure grid, and carrying out Gaussian field initialization on the Gaussian primitives according to a random sampling result to obtain an initialized Gaussian field; the loss between the current image frame and the RGB color image is calculated, parameters of Gaussian primitives of all the RGB image frames in the information are iteratively optimized in sequence according to the loss, all the optimized Gaussian primitives are combined, and a reconstructed three-dimensional scene is obtained. According to the invention, the spatial consistency and accuracy of reconstruction results are improved.

The invention discloses a network edge monitoring and early warning method based on video image AI analysis, and the method comprises the following steps: S1, obtaining video data, processing the video data, and generating an image frame sequence; s2, analyzing an image frame sequence, and extracting space and time features; s3, taking the target as a hypergraph node, constructing a hyperedge based on space and time features, and dynamically adjusting hyperedge connection by using a genetic algorithm; s4, constructing a multi-layer hypergraph Transform network, extracting multi-scale spatial-temporal characteristics, and calculating a semantic relationship between nodes; s5, setting a butterfly optimization algorithm initial population, and dynamically optimizing model parameters through global and local search; s6, constructing an anomaly detection model, identifying an abnormal behavior, and feeding back a result to optimize model parameters and hyperedge selection; and S7, deploying the model at an edge node, triggering early warning when an abnormal behavior is detected, and pushing information to a management platform. According to the invention, through video image AI analysis, accurate detection and real-time early warning of abnormal behaviors in a network edge scene are realized.

The invention discloses a light guide plate defect detection method and system based on a neural network, and particularly relates to the technical field of machine vision detection, and the method comprises the following steps: aiming at the problem of image instability of a light guide plate in a dynamic transmission or rotation process, continuously collecting an image sequence and extracting time domain features; and performing interference judgment in combination with the inter-frame consistency prediction coefficient and a first threshold to realize accurate identification of the abnormal image frame. For an abnormal image frame, further correcting the recognition credibility of the abnormal image frame by adopting a confidence adjustment and fusion mode, and meanwhile, introducing a frequency domain transformation and image enhancement strategy to compensate detail loss caused by motion blur; according to the method, inter-frame consistency analysis, confidence fusion regulation and control and frequency domain fuzzy recognition and compensation mechanisms are introduced, abnormal judgment and image quality restoration of the light guide plate image in the dynamic scene are realized, the recognition accuracy and stability of the neural network model on the defect type, position and confidence are improved, and the false detection and omission ratio is effectively reduced.

Disclosed is a nighttime unmanned aerial vehicle (UAV) object tracking method fusing a hybrid attention mechanism. According to the method, a night vision image enhancement model is employed to enhance various nighttime image frames in a night vision video sequence of UAV before performing image object tracking and recognition, to improve the accuracy of object tracking for nighttime images. With the aid of a spatial channel hybrid attention mechanism, the night vision image enhancement model can adaptively learn spatial channel features, enhancing generalization ability, and enabling the network model to focus on more effective features. Additionally, a denoising processing module is introduced to remove noise hidden in the nighttime images, improving the object positioning accuracy and tracking performance of UAV in nighttime scenarios, and reducing the failure probability of object tracking by UAV in nighttime scenarios.

The invention relates to the field of excavation equipment obstacle avoidance based on image analysis, in particular to a coal mine excavation equipment obstacle avoidance method and system based on machine vision, and the method comprises the steps: obtaining the frequency domain characteristic parameters of the current electromagnetic noise during the operation of underground electromechanical equipment, and synchronously collecting the current original image frame output by a visual sensor; after a current de-noised image frame is obtained after dynamic de-noising processing, an obstacle contour feature point set is extracted through multi-scale edge detection, and an obstacle position confidence map is obtained according to space-time consistency analysis; obtaining point cloud coordinates of an image data loss area caused by electromagnetic interference, and mapping the point cloud coordinates to an image coordinate system to obtain a three-dimensional geometric complemented image; and constructing a dynamic occupation grid map of the underground environment according to the three-dimensional geometric complementation image and the obstacle position confidence map, and planning an obstacle avoidance path of the mining equipment according to the dynamic occupation grid map. And the obstacle avoidance capability of the mining equipment is improved, and safe and efficient coal mining operation is guaranteed.

The invention discloses a concrete crack three-dimensional reconstruction method and system based on multi-modal fusion and a medium, and relates to the technical field of structural engineering detection, and the method comprises the following steps: obtaining a structural image frame sequence and a structural laser radar point cloud frame sequence of a target structure; obtaining a crack mask image frame after crack segmentation; an integral structure de-noised point cloud picture is obtained; obtaining a visible point cloud coloring point cloud map and a visible point cloud semantic segmentation point cloud map; obtaining an overall three-dimensional coloring point cloud map and an overall three-dimensional semantic segmentation point cloud map; obtaining an overall three-dimensional point cloud marking map; and performing three-dimensional attribute measurement on the crack based on the three-dimensional point cloud marking map to obtain three-dimensional geometric information of the crack. According to the method provided by the invention, a multi-frame and multi-modal fused crack structure reconstruction framework is designed, the method can adapt to crack detection of various three-dimensional structures, and simultaneous detection of crack width information, crack position and crack trend information can be realized based on an overall three-dimensional point cloud marking map.

The invention provides a space interaction accurate recognition method and system based on multi-modal fusion, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting human skeleton, visual image and voice information, generating a space-time attention map through employing a feature extraction network, enhancing visual features, and executing feature complementary correction. Fusing the three types of modal information to obtain unified feature representation; when the recognition confidence is low, the space-time convolution generative adversarial network guided based on the action causal relationship graph complements the missing image frame, and the accuracy and robustness of space interaction recognition are improved.

The invention discloses a transformer substation inspection robot positioning and mapping method based on point-line feature fusion. According to the method, a binocular camera is used for collecting point-line features in an image frame, and high-quality long-line features are quickly obtained based on an adaptive criterion optimized EDLines algorithm; the spatial depth of point-line features is obtained through triangularization of a binocular camera, and redundant line features are filtered based on a length response threshold strategy of the spatial depth of a transformer substation. Based on a point-line pole feature pole plane constraint method, IMU information and visual information are combined, gyroscope zero offset is independently estimated and optimized, IMU pre-integration information is updated, and a more accurate initial posture of the inspection robot is obtained. A point-line feature dictionary based on a transformer substation scene and a large-scale equipment target detection model in a transformer substation are made, closed-loop detection is performed through transformer substation equipment road signs and point-line features, and rapid relocation correction is performed on sliding windows of a current key frame and a previous fixed key frame number. And finally, the pose of the substation inspection robot and a substation scene map are optimized through global BA, and the problem of large-scale scene error drift accumulation is solved.

The invention relates to the technical field of nondestructive testing, in particular to a leak detection and partial discharge digital imaging detection method and system based on acousto-optic fusion, and the method comprises the following steps: based on channel microphone array sound wave data in a partial discharge signal suspicious region, extracting a sound wave abnormal section, positioning a sound source, matching image edge features, and synchronously marking; and analyzing the phase change of the multi-frequency signal to judge a sound source concentration area, tracking the moving trend of a disturbance point, and outputting an acousto-optic fusion positioning trend track. According to the method, the partial discharge feature recognition sensitivity is improved through high-frequency peak paragraph screening and dominant frequency recognition, the abnormal region positioning precision is enhanced in combination with image edge extraction and sound source space matching, and acousto-optic synchronous positioning and trend trajectory display are achieved through multi-frequency signal phase analysis and image frame disturbance tracking. Through fusion of frequency domain feature extraction, image recognition, dynamic comparison and other actions, the spatial precision of abnormal source recognition and the multi-source fusion analysis efficiency are improved, and the partial discharge traceability and dynamic monitoring capability are enhanced.

A method includes obtaining image frames of a scene captured using one or more imaging sensors of a video see-through (VST) extended reality (XR) device and depth data associated with the scene. The image frames capture a moving object and static scene contents, and the moving object includes a portion of a body of a user. The method also includes generating masks associated with the moving object using a machine learning model trained to separate pixels corresponding to human skin from other portions of the scene. The method further includes reconstructing images of the moving object and images of the static scene contents. In addition, the method includes combining the images of the moving object, the images of the static scene contents, and one or more virtual features to generate combined images and rendering the combined images for presentation on at least one display of the VST XR device.

The invention relates to the technical field of image steady state adjustment, and provides a self-adaptive image stabilization and dynamic horizontal reference calibration method and device, equipment and a medium. Time domain alignment and motion parameter interpolation processing are carried out on original sensor data to obtain a synchronized motion parameter data stream, and multi-sensor data fusion filtering is carried out on the synchronized motion parameter data stream to obtain equipment three-dimensional attitude parameters. The method comprises the following steps: performing motion mapping on an original image frame according to equipment three-dimensional attitude parameters to obtain an image displacement compensation matrix, performing feature tracking and pixel resampling on the original image frame according to the image displacement compensation matrix to obtain a preliminary stable image set, and performing horizon detection and rotation compensation on the preliminary stable image set to obtain a horizontal reference alignment image set; and performing time sequence weighted fusion on the horizontal reference alignment image set to obtain a steady-state video data stream. Through multi-sensor fusion, high-precision mapping, image adaptive calibration and weighted fusion, the precision of motion jitter suppression and horizontal calibration is improved.

The invention discloses a space-frequency domain graph learning-based adaptive deep fake face detection method and system, and the method comprises the steps: randomly extracting an image frame from a video, intercepting a face image, adjusting the feature dimension of the face image, and transmitting the face image to a depth adaptive wavelet module and a normalized residual homomorphic composition neural network module; a depth adaptive wavelet module extracts frequency features of the face image; a normalized residual homograph neural network module extracts spatial domain features of the face image; performing weighted fusion on the frequency domain features and the spatial domain features by using a self-adaptive feature fusion module based on gated convolution, realizing class attention guidance by using the gated convolution, dynamically adjusting the channel of a feature map and the weight of a spatial dimension, and finally obtaining fusion features; and performing classification according to the fusion features by using a classifier. According to the method, the extraction capability of forged detail clues is enhanced, and the detection precision and stability of the model are remarkably improved.

An action localization method, device, electronic equipment, and computer-readable storage medium are provided. The action localization method includes: identifying at least one target video segment containing a target object in a video; acquiring a first action recognition result of at least one image frame in the at least one target video segment and a second action recognition result of the target video segment; and acquiring an action localization result of the video based on the first action recognition result and the second action recognition result.

The invention relates to the technical field of anomaly detection, in particular to an intelligent gas hidden danger detection system which comprises a leakage monitoring module, a time difference extraction module, a direction positioning module, a color temperature analysis module and a risk aggregation module. According to the method, the time and space relation of gas concentration response nodes is extracted, a leakage diffusion path is constructed, and a dominant propagation direction is identified, so that a leakage trend region has a clear positioning basis, regional brightness and hue changes in an image frame are combined, the time sequence stability proportion is counted, and a cross-modal trend verification channel is established; the defect of a single sensing dimension is effectively made up, in the image and trend direction matching process, abnormal area numbers are recognized through stable proportion difference value changes, intersection confirmation is carried out on trend areas, the accuracy and spatial directivity of hidden danger marking are remarkably improved, the risk recognition chain is more complete through cooperative calculation of multi-source data, and the accuracy of hidden danger marking is improved. And the method has higher linkage response capability and dynamic judgment depth.

The invention relates to the field of image recognition, in particular to a wind power construction intelligent safety management method and system based on intelligent AI monitoring. The method comprises the following steps: obtaining an omnibearing real-time image flow of a wind power construction area, carrying out super-resolution deep convolution optimization and operator three-dimensional image segmentation, and extracting an operator three-dimensional image frame; three-dimensional point cloud modeling of the construction area is carried out based on the image flow, real-time image frame position positioning rendering is carried out according to a three-dimensional image frame, and a real-time twinborn model of the construction area is constructed; performing operation dynamic behavior analysis and behavior deviation degree quantitative analysis based on a twin model to obtain the behavior deviation degree of the operator; and according to the behavior deviation degree, carrying out early prediction analysis on illegal behaviors, making an adaptive risk early warning decision, and constructing an operation behavior risk early warning strategy. According to the invention, through real-time operation behavior identification and environmental risk analysis, the intelligence and safety level of wind power construction are improved.

The invention relates to the technical field of target detection, and discloses a pet target detection method and device and a camera, and the method comprises the steps: carrying out the motion triggering collection and image enhancement preprocessing of a front end region of a feeder, and obtaining an enhanced image frame sequence; performing feature extraction of dynamic receptive field adjustment on the enhanced image frame sequence to obtain pet feature descriptors and position information; behavior time sequence feature analysis is executed, and pet behavior sequence feature vectors are obtained; constructing a state transition diagram according to the pet behavior sequence feature vector, and performing time sequence consistency analysis to obtain a pet state judgment result; power management and decision execution are carried out on the feeder based on the pet state judgment result, feeding control under the low-power-consumption condition is achieved, behavior misjudgment caused by posture fluctuation is effectively avoided, the behavior recognition accuracy is improved, the accurate feeding control problem in a multi-pet family is solved, and the user experience is improved.

The invention provides an unmanned aerial vehicle multi-channel image transmission optimization system based on link quality perception so as to improve the transmission stability and the image quality guarantee capability of image data in a complex wireless environment. The prediction module constructs a time sequence model based on the historical link quality index of the wireless channel, and outputs a future link stability prediction value; the modeling module analyzes texture change and the like between the image frames, generates an image frame evolution vector, and calculates the aging sensitivity and reconstruction importance score of the image frames according to the image frame evolution vector; the decision-making module fuses the information and generates an optimal matching relation between the image frame and the channel and a transmission priority parameter; the grouping module performs image frame clustering according to the similarity between the priority parameters and the evolution vectors, constructs compression groups and generates corresponding compression configuration files and compression image data; the transmission module schedules the compressed image data to a corresponding wireless channel for transmission; the system can be widely applied to an unmanned aerial vehicle image transmission task with relatively high requirements on real-time performance and image quality in a dynamic environment.